# Noiseprint: a CNN-based camera model fingerprint

arXiv: https://arxiv.org/abs/1808.08396 · HTML: https://mlsanity.com/paper/1808.08396/

## Key facts

- Cited by 114 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 30 of 114)

- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Generalizable Synthetic Image Detection via Language-guided Contrastive Learning](https://mlsanity.com/paper/2305.13800/) (2023)
- [AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics](https://mlsanity.com/paper/2304.06870/) (2023)
- [Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales](https://mlsanity.com/paper/2107.05821/) (2021)
- [Learning Second Order Local Anomaly for General Face Forgery Detection](https://mlsanity.com/paper/2209.15490/) (2022)
- [D3: Training-Free AI-Generated Video Detection Using Second-Order Features](https://mlsanity.com/paper/2508.00701/) (2025)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [TransForensics: Image Forgery Localization with Dense Self-Attention](https://mlsanity.com/paper/2108.03871/) (2021)
- [Weakly-supervised deepfake localization in diffusion-generated images](https://mlsanity.com/paper/2311.04584/) (2023)
- [Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation](https://mlsanity.com/paper/2506.16802/) (2025)
- [TGIF: Text-Guided Inpainting Forgery Dataset](https://mlsanity.com/paper/2407.11566/) (2024)
- [Toward Generalizable Forgery Detection and Reasoning](https://mlsanity.com/paper/2503.21210/) (2025)
- [EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata](https://mlsanity.com/paper/2301.04647/) (2023)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion](https://mlsanity.com/paper/2309.09306/) (2023)
- [Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization](https://mlsanity.com/paper/2406.13565/) (2024)
- [ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.10238/) (2024)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering](https://mlsanity.com/paper/2308.09307/) (2023)
- [Open Set Synthetic Image Source Attribution](https://mlsanity.com/paper/2308.11557/) (2023)
- [Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review](https://mlsanity.com/paper/2502.15176/) (2025)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)

## Abstract (arXiv)

Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label -1) cameras. Although noiseprints can be used for a large variety of forensic tasks, here we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance.